Automatic projection focusing

ABSTRACT

Techniques are described for performing automatic focusing of a projected image on a mobile projection surface. Specific regions of the projected image are identified that are likely to be desired to stay in optimal focus, and attributes of those specific regions, such as sharpness and contrast, can be used to determine the need to refocus the image. Advanced knowledge of the image data being projected can be utilized to determine the specific regions of the projected image that require monitoring for optimal focusing.

RELATED APPLICATION

This application claims priority to U.S. Non-Provisional applicationSer. No. 13/612,173 to Soyannwo et al., entitled “Automatic ProjectionFocusing,” filed Sep. 12, 2012, the contents of which are incorporatedherein by reference in their entirety.

BACKGROUND

Existing projector systems typically project a dynamic and/or staticimage onto a surface and focus the image based autofocus algorithms thatmonitor one specific point or region of the image, e.g. a dead center ofthe projected image. This type of autofocus works sufficiently becausethe projection source is not expected to continuously change relative tothe projection surface. However, in the case of a highly mobileprojection surface or source, performing an autofocus without adverselyimpacting user experience becomes more difficult.

With the projector or surface reflecting the projected image in motion,the projected image may become distorted in any of three dimensions, andalso may be projected on a surface at various angles. Moreover, theprojected image typically has regions of interest to the viewer thatmight not be within the region of the image that the static autofocus ismonitoring. Consequently, the use of an autofocus on a static regionwithin the projected image may not adequately compensate for the motionof either the projector or surface, and does not necessarily keep theregion of most likely interest to the viewer of the image in focus.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 shows an illustrative augmented reality functional node (ARFN)configured to perform the techniques described herein for projectingimages into an augmented reality environment.

FIG. 2 shows additional details of an example ARFN that may beconfigured to provide a projected image.

FIG. 3 is an illustrative diagram of one embodiment of a projectionsystem that autofocuses the projected image based on the monitoring of aspecific region of interest.

FIG. 4 is a series of projected images in which specific regions of theimage that are likely not to be in motion are identified andspecifically monitored.

FIG. 5 is an illustrative flow diagram of one embodiment of theprojection autofocus process that determines one or more regions ofinterest based upon the motion detected within the projected image.

FIG. 6 is an illustrative flow diagram of one embodiment of theprojection autofocus process that determines one or more regions ofinterest based upon motion prediction within the image data that is tobe projected.

DETAILED DESCRIPTION

This disclosure describes systems and techniques for performing theautomatic focusing of a series of projected images where either theprojector, the surface reflecting the projected image, or both, arepotentially mobile. Specific regions of the projected images areidentified that are not in motion relative to other portions of theimages, and attributes of those specific regions, such as sharpness andcontrast, can be used to determine the need to refocus the image.

In one embodiment, the system includes one or more processors, and oneor more sensors communicatively coupled to the one or more processors,with the sensors including at least a camera. The system also includes aprojector, and causes the projector to project the series of images inthe form of a light pattern into an environment such that the lightpattern is detectable by the camera, and then the system determines atleast one specific region in the projected light pattern that is to bemonitored for automatically focusing the light pattern. The system thenmonitors the at least one specific region in the projected light patternfor a predetermined threshold of a change in an image attribute, andrefocuses the light pattern based upon the predetermined threshold beingmet.

In one embodiment, the system determines the region(s) of interest inthe projected light pattern based upon review of the image data thatcomprises the projected light pattern, and can do so before the lightpattern is projected or concurrently therewith. For instance, a cameracan identity a portion of the projected light pattern that has notchanged greatly over a number of frames and can use this portion todetermine whether the projector should be focused. In another example,this determination of can be based upon motion prediction within theimage data that comprises the projected light pattern. That is, thesystem may analyze the image date that is to be projected to determinewhich portion(s) of the projected light pattern can be used todetermined whether the projected should be refocused due to a changedlocation of a display medium. By selecting areas of the light patterncomprising the series of projected images that are generally not inmotion over a number of image frames, the camera can more easilyidentify any changes in focus in those areas.

Example Environments

FIG. 1 shows an illustrative augmented reality environment 100 thatincludes an augmented reality functional node (ARFN) 102 configured toperform the techniques described herein. When active, the ARFN node 102may project content onto any surface within the environment 100, such asa portable display medium 104, thereby generating an augmented realityenvironment that may incorporate real-world objects within theenvironment 100. The projected content may include electronic books,videos, images, interactive menus, or any other sort of visual and/oraudible content. The ARFN node 102 scans the environment 100 todetermine the presence of any objects, such a user 106 within theenvironment 100, and can project content 104, such as an image, on anysurface.

As illustrated, the ARFN node 102 comprises a computing device 108, aprojector 110, and one or more sensor(s) 112. Some or the all of thecomputing device 108 may reside within a housing of the ARFN node 102 ormay reside at another location that is operatively connected to the ARFNnode 102. The computing device 108 comprises one or more processor(s)114, an input/output interface 116, and storage media 118. Theprocessor(s) 114 may be configured to execute instructions that may bestored in the storage media 118 or in other storage media accessible tothe processor(s) 114.

As illustrated, the user 106 holds a portable display medium 104, ontowhich the projector 110 may project content for consumption by the user106. The sensor(s) 112 of the ARFN node 102 may capture images fordetecting a location and orientation or the medium 104 for the purposeof instructing the projector where to project the content within theenvironment, as well as to focus the projection when the imagesprojected onto the portable display medium become out of focus due tothe shifting of the display medium in the user's hands.

The input/output interface 116, meanwhile, may be configured to couplethe computing device 108 to other components of the ARFN node 102, suchas the projector 110, the sensor(s) 112, other ARFN nodes 102 (such asin other environments or in the environment 100), other computingdevices, sirens, network communication devices (such as modems, routers,and wireless transmitters), a conventional security system, and soforth. The coupling between the computing device 108 and other devicesmay be via wire, fiber optic cable, wireless connection, or the like.The sensors may include, in various embodiments, one or more imagesensors such as one or more cameras (motion and/or still cameras), audiosensors such as microphones, ultrasound transducers, heat sensors,motion detectors (including infrared imaging devices), depth sensingcameras, weight sensors, touch sensors, tactile output devices,olfactory sensors, temperature sensors, humidity sensors, and pressuresensors. Other sensor types and sensed attributes may be utilizedwithout departing from the scope of the present disclosure.

The storage media 118, meanwhile, may include computer-readable storagemedia (“CRSM”). The CRSM may be any available physical media accessibleby a computing device to implement the instructions stored thereon. CRSMmay include, but is not limited to, random access memory (“RAM”),read-only memory (“ROM”), electrically erasable programmable read-onlymemory (“EEPROM”), flash memory, or other memory technology, compactdisk read-only memory (“CD-ROM”), digital versatile disks (“DVD”) orother optical disk storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store the desired information and which can beaccessed by a computing device 108. The storage media 118 may residewithin a housing of the ARFN, on one or more storage devices accessibleon a local network, on cloud storage accessible via a wide area network,or in any other accessible location.

The storage media 118 may store several modules, such as instructions,datastores, and so forth that are configured to execute on theprocessor(s) 114. For instance, the storage media 118 may store anoperating system module 120, an interface module 122, a tracking module124, and an output module 126.

The operating system module 120 may be configured to manage hardware andservices within and coupled to the computing device 108 for the benefitof other modules. The interface module 122, meanwhile, may be configuredto receive and interpret commands received from users within theenvironment 100. For instance, the interface module 122 may analyze andparse images captured by one or more cameras of the sensor(s) 112 toidentify users within the environment 100 and to identify gestures madeby users within the environment 100, such as gesture commands to projectdisplay content. In other instances, the interface module 122 identifiescommands audibly issued by users within the environment and captured byone or more microphones of the sensor(s) 112. In still other instances,the interface module 122 allows users to interface and interact with theARFN node 102 in any way, such as via physical controls, and the like.

The tracking module 124, meanwhile, may be configured to track theportable display medium 104 through the environment 100. For example, ininstances where the user 106 carries the portable display medium 104through the environment for receiving projected content, the trackingmodule 124 may track the location (e.g., in three space) and orientationof the display medium within the environment. The output module 126,meanwhile, may be configured to receive this information from thetracking module 124, determine where to project content based on thisinformation, and periodically or continuously determine how to focus theprojector 110 based on the location and orientation of the displaymedium. For instance, and as described in detail below, the outputmodule 126 may determine how to focus a projected image on a displaymedium as the user 106 moves the display medium through the environment100.

The tracking module 124 may track the location and orientation of theportable display medium 104 using any type of depth-sensing technique.For instance, the projector may project a structured light pattern inthe environment, and the camera may capture distortion in the structuredlight due to objects in the environment, such as the portable displaymedium. The system may then analyze this distortion to determine objectswithin the environment, as well as the locations and orientations ofthese objects. Of course, while one example is provided, it is to beappreciated that the tracking module 124 may track the portable displaymedium using any type of depth-sensing technique, such as time-of-flightor the like.

Example ARFN

FIG. 2 shows additional details of an exemplary ARFN node 102 that maybe configured as described above with reference to FIG. 1. The ARFN node102 is configured to scan at least a portion of an environment 202 andthe objects therein to detect and identify, such as the location andorientation of a portable display medium as a user moves the mediumthrough the environment. The ARFN node 102 may also be configured toprovide augmented reality output, such as images, sounds, and so forth.

A chassis 204 holds the components of the ARFN node 102. One or moreprojector(s) 206 may be disposed within the chassis 204 and may beconfigured to generate and project light and/or images into theenvironment 202. These images may be visible light images perceptible toan object, visible light images imperceptible to the object, images withnon-visible light, or a combination thereof. This projector(s) 306 maybe implemented with any number of technologies capable of generating animage and projecting that image onto a surface within the environment.Suitable technologies include a digital micromirror device (DMD), liquidcrystal on silicon display (LCOS), liquid crystal display, 3LCD, and soforth. The projector(s) 206 has a projector field of view 308 whichdescribes a particular solid angle. The projector field of view 208 mayvary according to changes in the configuration of the projector. Forexample, the projector field of view 208 may narrow upon application ofan optical zoom to the projector.

In some implementations, the ARFN node 102 may instruct devices that areseparate from, but at least partially controllable by, the ARFN node 102to visually or audibly output content. For instance, the ARFN node 102may instruct a television or other display device within the environmentto output a particular piece of visual content. Or, the ARFN node 102may instruct stereo speakers within the environment to output certainaudible content. In these instances, the projector(s) 206 may projectnon-visible structured light (or other types of light for sensingobjects and objects and/or for sensing movement within the environment),while utilizing other output devices within the environment to outputrequested content, including content associated with one or moresecurity actions such as but not limited to augmented reality securitymeasures as described elsewhere within this Detailed Description. Ofcourse, in other instances the projector(s) 206 may be used instead ofor in addition to the existing output devices within the environment.

One or more camera(s) 210 may also be disposed within the chassis 204.The camera(s) 210 is configured to image the environment 202 in visiblelight wavelengths, non-visible light wavelengths, or both. The camera(s)210 has a camera field of view 212 that describes a particular solidangle. The camera field of view 212 may vary according to changes in theconfiguration of the camera(s) 210. For example, an optical zoom of thecamera may narrow the camera field of view 212.

In some implementations, a plurality of camera(s) 210 may be used. Forinstance, one embodiment of the ARFN node 102 may include athree-dimensional (3D), infrared (IR) camera and a red-green-blue (RGB)camera. The 3D, IR camera may be configured to capture information fordetecting depths of objects within the environment 202, while the RGBcamera may be configured to detect edges of objects by identifyingchanges in color within the environment 202. In some instances, a singlecamera may be configured to perform these functions.

The chassis 204 may be mounted with a fixed orientation, or may becoupled via an actuator to a fixture such that the chassis 204 may move.Actuators may include piezoelectric actuators, motors, linear actuators,and other devices configured to displace or move the chassis 204 orcomponents therein such as the projector(s) 206 and/or the camera(s)210. For example, in one implementation the actuator may comprise a panmotor 214, a tilt motor 216, and so forth. The pan motor 214 isconfigured to rotate the chassis 204 in a yawing motion. The tilt motor216, meanwhile, is configured to change the pitch of the chassis 204. Insome instances, the ARFN additionally or alternatively includes a rollmotor 218, which allows the chassis 204 to move in a rolling motion. Bypanning, tilting, and/or rolling the chassis 204, different views of theenvironment 202 may be acquired. Thus, the ARFN node 102 is mobile andthe projected image from projector 206 will be effected by motion of thenode 102.

One or more microphones 220 may be disposed within the chassis 204, orelsewhere within the environment 202. These microphones 220 may be usedto acquire input from an object, for echolocation, locationdetermination of a sound, or to otherwise aid in the characterization ofand receipt of input from the environment 202. For example, an objectmay make a particular noise, such as a cough, a throat clear, a tap on awall, or snap of the fingers, which are pre-designated as attentioncommand inputs or as object detection noises. Such audio inputs may belocated within the environment using time-of-arrival differences amongmultiple microphones and used to summon an active zone within theaugmented reality environment or identify a location of the object, andcan be to discern acoustic information from a surface. One or morespeaker(s) 222 may also be present to provide for audible output, suchas music, text-to-speech or the like.

A transducer 224 may be present within the ARFN node 102, or elsewherewithin the environment, and may be configured to detect and/or generateinaudible signals, such as infrasound or ultrasound. These inaudiblesignals may be used to provide for signaling between accessory devicesand the ARFN node 102.

The ARFN node 102 may also include a ranging system 226. The rangingsystem 226 is configured to provide distance information from the ARFNnode 102 to a scanned object, or other objects within the environment.The ranging system 226 may comprise radar, light detection and ranging(LIDAR), ultrasonic ranging, stereoscopic ranging, and so forth. In someimplementations the transducer 224, the microphones 220, the speaker(s)222, or a combination thereof may be configured to use echolocation orecho-ranging to determine distance and spatial characteristics of anobject.

In this illustration, the computing device 108 is shown within thechassis 204. However, in other implementations all or a portion of thecomputing device 108 may be disposed in another location and coupled tothe ARFN node 102. This coupling may occur via wire, fiber optic cable,wirelessly, or a combination thereof. Furthermore, additional resourcesexternal to the ARFN node 102 may be accessed, such as resources inanother ARFN node 102 accessible via a local area network, cloudresources accessible via a wide area network connection, or acombination thereof.

Also shown in this illustration is a projector/camera linear offsetdesignated “O”. This is a linear distance between the projector(s) 206and the camera(s) 210. Placement of the projector(s) 206 and thecamera(s) 210 at distance “O” from one another may aid in the recoveryof structured or other light data from the environment. The knownprojector/camera linear offset “O” may also be used to calculatedistances, dimensioning, and otherwise aid in the characterization ofobjects within the environment 202. In other implementations therelative angle and size of the projector field of view 208 and camerafield of view 212 may vary. Also, the angle of the projector(s) 206 andthe camera(s) 210 relative to the chassis 204 may vary.

Further illustrated in the embodiment of the node 102 in FIG. 2 are thefocus controls motors, shown here as focus motion 230, zoom motor 232and iris motor 234. Each of the motors can therefore either directly orindirectly control the focus of the image. The focus motor 230 can thusdirectly control the focus of the image camera 210 projects. The zoommotor 232 will zoom the image in an appropriate direction which canindirectly affect the focus of the projected image. The iris motor 234adjusts the amount of light being projected and thereby also indirectlyaffects the focus of the image.

It should further be noted that the tilt motor 216, pan motor 214 androll motor 218 can likewise adjust the position of the node 102 to causethe projected image to change in relation to the surface upon which theimage is projected, such as portable display medium 104 in FIG. 1. Othermotors can be used in the present node to adjust attributes of thecamera 210 or node 102 to otherwise directly or indirectly adjust thefocus of the projected image.

In other implementations, the components of the ARFN node 102 may bedistributed in one or more locations within the environment 100. Asmentioned above, microphones 220 and speaker(s) 222 may be distributedthroughout the environment. The projector(s) 206 and the camera(s) 210may also be located in separate chasses 204. The ARFN node 102 may alsoinclude discrete portable signaling devices used by objects to issuecommand attention inputs. For example, these may be acoustic clickers(audible or ultrasonic), electronic signaling devices such as infraredemitters, radio transmitters, and so forth.

The ARFN node 102 is shown in FIG. 2 with various sensors, but othersensors may be located either within or external to the chassis 204. Thesensors may include, in various embodiments, cameras (motion and/orstill cameras), audio sensors such as microphones, ultrasoundtransducers, heat sensors, motion detectors (including infrared imagingdevices), depth sensing cameras, weight sensors, touch sensors, tactileoutput devices, olfactory sensors, temperature sensors, humiditysensors, pressure sensors, and so forth.

FIG. 3 is an illustrative diagram of one embodiment of a projectionsystem 300 that autofocuses a series of projected images based on adistance and/or orientation of a display changing relative to aprojector that projects the images. For instance, FIG. 3 illustrates aprojector projecting content onto the portable display medium 104 beingheld by the user 106. Because the user may move the medium 104 whenconsuming the content, the location and/or orientation of the mediumrelative to the projector may change, thus necessitating a change infocus of a lens of the projector to maintain image quality and viewingconsistency for the user 106. As described below, the techniques maycontinuously or periodically autofocus the project based on themonitoring of a specific region of interest 310. This region of interestmay be defined as a portion of series of images (e.g., a video) that hasa relatively large degree of contrast around the edges and that changesvery little over a series of frames. Given the contrast and staticnature of such a region, the system may be able to accurately identifyany changes in image quality due to an out-of-focus projection (due tochanges in the location or orientation of the medium 104).

In this instant illustration, the projector 302 projects a projectedimage 304 onto the display medium 104. In some implementations asequence of different projected images 304 may be used (e.g., a seriesof frames that collectively define a video). The distance orientation ofthe surface 104 relative to the projector 302 may change as theprojector projects the image(s) 304 onto the surface 104 and, therefore,the focus of the projector 302 may need to dynamically change. Asdescribed above, this position and orientation of the surface 104relative to the projector may be determined in any number of ways, suchas via structured light, LIDAR, time-of-flight techniques or the like.

For illustrative purposes, a person is the region of interest 310 and isshown within the projected image 304. That is, this region of interest310 may represent a portion of the sequence of images that does notchange greatly over a period of time (i.e., has little motion) and thathas a relatively large contrast around its edges. In this embodiment,the source image data to be projected (e.g., the video to be projected)is provided to an auto-focus engine 314 and is used to reference imagedata to initially determine what will constitute the region of interest,such as the person as the region of interest 310. In other words, theauto-focus engine 314 will predict, through motion analysis of thesource image data, that the person in the projected image 304 is doesnot move in the images over a sequence of frames. The rendering engine312 provides input from the image source (which can be raw buffer ormotion vector information from video) going through projector 302 to theauto-focus engine 314, which can then be synchronized with a frame syncpulse from the projector 302 to effect refocus of the region of interest310, if necessary. The camera 306 will monitor such attributes assharpness, contract, brightness, etc., of the projected image 304 andinteract with the projector 302 to start focusing the image throughadjustable lens 308.

As the projected image 304 is refocused, the camera 306 can also thencapture the region of interest 310 attributes again and relays them tothe auto focus engine 314 to determine if the refocusing is making theregion of interest better for viewing. As described in more detailedbelow, a threshold of an attribute is predetermined, such as a specificlevel of sharpness or contrast, and the refocusing of the image is donebased upon the threshold. If the region of interest 310 is distorted bythe surface 104 having imperfections therein, or through the surface 104being at a non-substantially-orthogonal relation to the projector 302, abaseline can be set at the first few frames of content display such thatauto-focus engine 314 can account for the baseline distortion infocusing the projected image 304.

Further, the autofocusing can occur from either directly adjusting thefocus of the image 304, such as adjusting lens 308, or can also occurthrough indirect methods, such as zooming the image (such as with zoommotor 232 in FIG. 2), adjusting the amount of light being projected(such as with iris motion 234), or by adjusting the positioning of theprojector 302. These adjustments can be made at the same time, orseparately. Any methods of directly or indirectly affecting the focus ofthe projected image 304 known to one of skill in the art can be utilizedin the projection system 300 to assist in optimally focusing theprojected image 304.

FIG. 4 represents a series of images that collectively form a portion ofa video that may be projected onto a portable display medium. Using thetechniques described above, regions of these images having a relativelyhigh contrast may be identified for the potential use of monitoringthese regions over time to determine whether the projector should bere-focused.

FIG. 4 illustrates a series of projected images A-F in which specificregions 1-3 of the image that have high contrast therein and, therefore,may be monitored for purposes of autofocusing. In this video sequence, apredefined number of consecutive frames A-F are captured either viaimage data processing on the incoming video stream or through the camerasensing the live display (e.g., frame by frame), and the data isprocessed to determine a region of interest for monitoring and potentialrefocusing. The first image A is used as a background image and thepotential areas of interest are identified, here area 1, 2, and 3. Themost ideal regions are selected here based on the contrast score versusthe initial background. In the frame A, each selected regions 1, 2 and 3are compared for significant change in contrast/scenery and thus standout from the background. Then significant motion changes are notedwithin the selected regions as the frame rate progresses, and the samesteps are repeated for B, C, D and E to create, for instance, a 5 framesample.

At the end of the sampling period, the algorithm is able to determinewhich regions remain mostly consistent, e.g. 1 and 3, and which is inmotion, e.g. frame 2. The contrast score or sharpness algorithm is thenrun on the regions having the high contrast and yet do not include agreat degree of motion. Here, region 2 can be may be discarded given itslarge degree of motion through the sequence, while region 1 & 3 are usedto determine whether the projector should be refocused. In such anembodiment, the final focus score is used as input to determine if theimage is in focus.

If focusing is needed based upon the algorithm used, the lens 308 inFIG. 3 will adjust the image, and the same steps are repeated until aconvergence is realized and the threshold values of contrast andsharpness are reached. It should be further noted that the number ofpotential regions of interest is not limited and other algorithms can beused to determine when the image is out of focus.

Example Processes

FIG. 5 is an illustrative flow diagram of one embodiment of theprojection autofocus process 500 that determines one or more regions ofinterest based upon the motion detected within the projected image. Atstep 502, a light pattern representing a series of one or more images(such as projected image 304 in FIG. 3) is projected onto a surface(such as the display medium 104). At step 504, the light pattern ismonitored. For example, frames A-F can initially monitored as shown inFIG. 4. Then a determination is made as to whether one or more specificregions of the image stand out from the background and include an amountof motion that is less than a threshold, as shown at decision 506.

If no specific regions of interest are identified at decision 506, thenthe process iterates to again monitor the light pattern at step 504. Forexample, if no regions of interest existed in the first 5 frames becausethere was not sufficient contrast or because each region having asufficiently high contrast contained too much motion, then the processiterates to monitor the next 5 frames. If one or more regions ofinterest can be identified at decision 506, then the specific region isidentified as a specific region of interest for the next monitoringperiod, as shown at step 508, and is then monitored throughout the nextmonitoring period as shown at step 510, e.g. the next 5 frames.

A determination is then made as to whether the monitored region(s) ofinterest is changing beyond a predetermined threshold, as shown atdecision 512. The predetermined threshold can be a minimum level ofsharpness, contrast, or any other attribute indicative of the focus ofthe image. If the region(s) of interest have not changed beyond thethreshold in decision 512, then the process iterates to again monitorthe light pattern at decision 504. Otherwise, if the region(s) ofinterest has changed at decision 512, then the projection is refocusedas shown at step 514 and the process iterates at decision 512 until theone or more regions of interest are in adequate focus again.

FIG. 6 is an illustrative flow diagram of one embodiment of theprojection autofocus process 600 that determines the region of interestbased upon motion prediction based upon the image data that is to beprojected. This process can be performed by the projection system 300 inFIG. 3 with the rendering engine 312 and auto-focusing engine 314. Theincoming image data is detected as shown at step 602 and, as in theexample in FIG. 3, the rendering engine 312 will send the incoming imagedata to the autofocus engine 314. Thereafter, motion within the imagedata is that is to be projected is determined, as shown at step 604.

Once the motion is predicted within the image at step 604, one or moreregion of interest are designated as shown at step 606. As describedabove, these region(s) of interest comprise those regions having asufficiently high level of contrast and yet include little motion over anumber of frames. After identifying the regions of interest, the imagedata is projected on the surface (such as on the display medium 104 inFIG. 3) as shown at step 608. The region(s) of interest in the projectedimage are then monitored as shown at step 610. Thus, with reference tothe projection system 300 of FIG. 3, the camera 306 will monitor theprojected image 304 and regions of interest (such as regions 1-3 ofimages A-F in FIG. 4), and such monitoring will typically occur for aset period of frames as has been described herein.

After the monitoring period is complete at step 610, then adetermination is made as to whether the monitored region(s) of interestis changing beyond a predetermined threshold, as shown at decision 612.The predetermined threshold can be a minimum level of sharpness,contrast, or any other attribute indicative of the focus of the image.If the region(s) of interest have not changed beyond the threshold indecision 612, then the process iterates to again detect the incomingimage data at step 602 and start to generate the region(s) of interestfor monitoring. Otherwise, if the region(s) of interest has changed atdecision 612, then the projection is refocused as shown at step 614 andthe process iterates at decision 612 until the one or more regions ofinterest are in adequate focus again.

CONCLUSION

Although the subject matter has been described in language specific tostructural features, it is to be understood that the subject matterdefined in the appended claims is not necessarily limited to thespecific features described. Rather, the specific features are disclosedas illustrative forms of implementing the claims.

What is claimed is:
 1. A system comprising: one or more processors; acamera; a projector; and one or more non-transitory computer-readablestorage media storing computer-executable instructions that areexecutable by the one or more processors to cause the one or moreprocessors to: cause the projector to project an image on a surface;capture image information of the image, the image informationrepresentative of one or more objects within the image; determine motioninformation associated with the image based at least in part on theimage information; determine, based at least in part upon the motioninformation, a region in the image for monitoring; detect that a changein an image attribute associated with the region meets or exceeds afirst threshold; and adjust a focus parameter of the projector andproject the image on the surface using the focus parameter, the focusparameter including at least one of a position setting or a motorsetting of the projector.
 2. The system of claim 1, wherein theinstructions are further executable to: determine the region in theimage based at least in part upon review of data captured by one or moresensors and associated with the surface.
 3. The system of claim 2,wherein the instructions are further executable to: determine estimatedmotion for an object of the one or more objects depicted in the imagebased upon image data associated with the image; and determine theregion based at least in part upon the estimated motion.
 4. The systemof claim 1, wherein the instructions are further executable to: projecta plurality of images on the surface, the plurality of images includingthe image; determine motion of an object of the one or more objectswithin the region of each of the plurality of images; determine that anamount of the motion is less than a second threshold; and determine theregion, based at least in part upon the amount of the motion being lessthan the second threshold.
 5. The system of claim 1, wherein theinstructions are further executable to, after adjusting the focusparameter: change the first threshold to a third threshold for theregion.
 6. The system of claim 1, wherein the instructions are furtherexecutable to, after adjusting the focus parameter: change a frequencyof monitoring the region.
 7. The system of claim 1, wherein theinstructions are further executable to: determine that the image isdistorted by the surface; establish a baseline distortion of the image;establish a second threshold based at least in part on the baselinedistortion; and detect that a second change in the image attributeassociated with the region meets or exceeds the second threshold.
 8. Amethod comprising: under control of one or more computer systemsconfigured with executable instructions; projecting an image on asurface; capturing data associated with the image; selecting a regionassociated with the image to be monitored; detecting a change in animage attribute of the image within the region, the change greater thana threshold associated with the image attribute; and adjusting a focusof a projector based at least in part on the threshold being met orexceeded.
 9. The method of claim 8, further comprising: adjusting thethreshold based at least in part on the adjusting of the focus of theprojector.
 10. The method of claim 8, further comprising: monitoring theregion at periodic time intervals.
 11. The method of claim 8, furthercomprising: determining that the projected image is distorted by anamount; and adjusting the threshold based at least in part on theamount.
 12. The method of claim 8, further comprising: changing a timeinterval associated with periodic monitoring of the region.
 13. Thesystem of claim 8, further comprising: projecting a light pattern on thesurface; and determining the region based at least in part on datacaptured by one or more sensors associated with the light patternprojected on the surface.
 14. A computing device comprising: one or moreprocessors; a sensor to capture image data; a projector to project animage onto a surface; and one or more non-transitory computer-readablestorage media storing computer-executable instructions that areexecutable by the one or more processors to cause the one or moreprocessors to: determine, from the image data, a region in the imagethat is to be monitored; and adjust an attribute of the image based atleast in part on detecting a reduction in image quality greater than orequal to the threshold.
 15. The computing device of claim 14, whereinthe surface is a portable display medium and the instructions arefurther executable to: track a movement of the portable display medium;and cause the projector to continually project the image on the portabledisplay medium.
 16. The computing device of claim 15, further comprisingadjust a focus parameter of the projector to adjust the attribute, thefocus parameter including at least one of a position setting or a motorsetting of the projector.
 17. The computing device of claim 16, whereinthe instructions are further executable to: cause the sensor to capturethe image data at periodic intervals.
 18. The computing device of claim14, wherein the instructions are further executable to: cause theprojector to project a light pattern on the surface; and determine theregion based at least in part upon analyzing image data associated withthe light pattern.
 19. The computing device of claim 14, wherein thesensor includes at least one of: one or more image sensors; one or morecameras; one or more microphones; one or more ultrasound transducers;one or more heat sensors; one or more motion detectors; or one or moredepth sensing cameras.
 20. The computing device of claim 14, wherein theinstructions are further executable to: adjust the threshold based atleast in part on the adjusting the attribute.